Journal of Applied Crystallography最新文献

{"title":"Quantitative selection of sample structures in small-angle scattering using Bayesian methods.","authors":"Yui Hayashi, Shun Katakami, Shigeo Kuwamoto, Kenji Nagata, Masaichiro Mizumaki, Masato Okada","doi":"10.1107/S1600576724004138","DOIUrl":"10.1107/S1600576724004138","url":null,"abstract":"<p><p>Small-angle scattering (SAS) is a key experimental technique for analyzing nanoscale structures in various materials. In SAS data analysis, selecting an appropriate mathematical model for the scattering intensity is critical, as it generates a hypothesis of the structure of the experimental sample. Traditional model selection methods either rely on qualitative approaches or are prone to overfitting. This paper introduces an analytical method that applies Bayesian model selection to SAS measurement data, enabling a quantitative evaluation of the validity of mathematical models. The performance of the method is assessed through numerical experiments using artificial data for multicomponent spherical materials, demonstrating that this proposed analysis approach yields highly accurate and interpretable results. The ability of the method to analyze a range of mixing ratios and particle size ratios for mixed components is also discussed, along with its precision in model evaluation by the degree of fitting. The proposed method effectively facilitates quantitative analysis of nanoscale sample structures in SAS, which has traditionally been challenging, and is expected to contribute significantly to advancements in a wide range of fields.</p>","PeriodicalId":14950,"journal":{"name":"Journal of Applied Crystallography","volume":"57 Pt 4","pages":"955-965"},"PeriodicalIF":6.1,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11299617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking copper nanofiller evolution in polysiloxane during processing into SiOC ceramic.","authors":"Patricia A Loughney, Paul Cuillier, Timothy L Pruyn, Vicky Doan-Nguyen","doi":"10.1107/S1600576724003133","DOIUrl":"10.1107/S1600576724003133","url":null,"abstract":"<p><p>Polymer-derived ceramics (PDCs) remain at the forefront of research for a variety of applications including ultra-high-temperature ceramics, energy storage and functional coatings. Despite their wide use, questions remain about the complex structural transition from polymer to ceramic and how local structure influences the final microstructure and resulting properties. This is further complicated when nanofillers are introduced to tailor structural and functional properties, as nanoparticle surfaces can interact with the matrix and influence the resulting structure. The inclusion of crystalline nanofiller produces a mixed crystalline-amorphous composite, which poses characterization challenges. With this study, we aim to address these challenges with a local-scale structural study that probes changes in a polysiloxane matrix with incorporated copper nanofiller. Composites were processed at three unique temperatures to capture mixing, pyrolysis and initial crystallization stages for the pre-ceramic polymer. We observed the evolution of the nanofiller with electron microscopy and applied synchrotron X-ray diffraction with differential pair distribution function (d-PDF) analysis to monitor changes in the matrix's local structure and interactions with the nanofiller. The application of the d-PDF to PDC materials is novel and informs future studies to understand interfacial interactions between nanofiller and matrix throughout PDC processing.</p>","PeriodicalId":14950,"journal":{"name":"Journal of Applied Crystallography","volume":"57 Pt 4","pages":"945-954"},"PeriodicalIF":6.1,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11299600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implications of size dispersion on X-ray scattering of crystalline nanoparticles: CeO₂ as a case study.","authors":"Adriana Valério, Fabiane J Trindade, Rafaela F S Penacchio, Bria Cisi, Sérgio Damasceno, Maurício B Estradiote, Cristiane B Rodella, Andre S Ferlauto, Stefan W Kycia, Sérgio L Morelhão","doi":"10.1107/S1600576724003108","DOIUrl":"10.1107/S1600576724003108","url":null,"abstract":"<p><p>Controlling the shape and size dispersivity and crystallinity of nanoparticles (NPs) has been a challenge in identifying these parameters' role in the physical and chemical properties of NPs. The need for reliable quantitative tools for analyzing the dispersivity and crystallinity of NPs is a considerable problem in optimizing scalable synthesis routes capable of controlling NP properties. The most common tools are electron microscopy (EM) and X-ray scattering techniques. However, each technique has different susceptibility to these parameters, implying that more than one technique is necessary to characterize NP systems with maximum reliability. Wide-angle X-ray scattering (WAXS) is mandatory to access information on crystallinity. In contrast, EM or small-angle X-ray scattering (SAXS) is required to access information on whole NP sizes. EM provides average values on relatively small ensembles in contrast to the bulk values accessed by X-ray techniques. Besides the fact that the SAXS and WAXS techniques have different susceptibilities to size distributions, SAXS is easily affected by NP-NP interaction distances. Because of all the variables involved, there have yet to be proposed methodologies for cross-analyzing data from two techniques that can provide reliable quantitative results of dispersivity and crystallinity. In this work, a SAXS/WAXS-based methodology is proposed for simultaneously quantifying size distribution and degree of crystallinity of NPs. The most reliable easy-to-access size result for each technique is demonstrated by computer simulation. Strategies on how to compare these results and how to identify NP-NP interaction effects underneath the SAXS intensity curve are presented. Experimental results are shown for cubic-like CeO₂ NPs. WAXS size results from two analytical procedures are compared, line-profile fitting of individual diffraction peaks in opposition to whole pattern fitting. The impact of shape dispersivity is also evaluated. Extension of the proposed methodology for cross-analyzing EM and WAXS data is possible.</p>","PeriodicalId":14950,"journal":{"name":"Journal of Applied Crystallography","volume":"57 Pt 3","pages":"793-807"},"PeriodicalIF":6.1,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11151675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141283744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subperiodic groups, line groups and their applications.","authors":"Gemma de la Flor, Ivanka Milošević","doi":"10.1107/S1600576724003418","DOIUrl":"10.1107/S1600576724003418","url":null,"abstract":"<p><p>Understanding the symmetries described by subperiodic groups - frieze, rod and layer groups - has been instrumental in predicting various properties (band structures, optical absorption, Raman spectra, diffraction patterns, topological properties <i>etc</i>.) of 'low-dimensional' crystals. This knowledge is crucial in the tailored design of materials for specific applications across electronics, photonics and materials engineering. However, there are materials that have the property of being periodic only in one direction and whose symmetry cannot be described by the subperiodic rod groups. Describing the symmetry of these materials necessitates the application of line group theory. This paper gives an overview of subperiodic groups while briefly introducing line groups in order to acquaint the crystallographic community with these symmetries and direct them to pertinent literature. Since line groups are generally not sub-periodic, they have thus far remained outside the realm of symmetries traditionally considered in crystallography, although there are numerous 'one-dimensional' crystals (<i>i.e.</i> monoperiodic structures) possessing line group symmetry.</p>","PeriodicalId":14950,"journal":{"name":"Journal of Applied Crystallography","volume":"57 Pt 3","pages":"623-629"},"PeriodicalIF":6.1,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11151676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141283746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data.","authors":"Victor Poline, Ravi Raj Purohit Purushottam Raj Purohit, Pierre Bordet, Nils Blanc, Pauline Martinetto","doi":"10.1107/S1600576724003704","DOIUrl":"10.1107/S1600576724003704","url":null,"abstract":"<p><p>Recent developments in synchrotron radiation facilities have increased the amount of data generated during acquisitions considerably, requiring fast and efficient data processing techniques. Here, the application of dense neural networks (DNNs) to data treatment of X-ray diffraction computed tomography (XRD-CT) experiments is presented. Processing involves mapping the phases in a tomographic slice by predicting the phase fraction in each individual pixel. DNNs were trained on sets of calculated XRD patterns generated using a Python algorithm developed in-house. An initial Rietveld refinement of the tomographic slice sum pattern provides additional information (peak widths and integrated intensities for each phase) to improve the generation of simulated patterns and make them closer to real data. A grid search was used to optimize the network architecture and demonstrated that a single fully connected dense layer was sufficient to accurately determine phase proportions. This DNN was used on the XRD-CT acquisition of a mock-up and a historical sample of highly heterogeneous multi-layered decoration of a late medieval statue, called 'applied brocade'. The phase maps predicted by the DNN were in good agreement with other methods, such as non-negative matrix factorization and serial Rietveld refinements performed with <i>TOPAS</i>, and outperformed them in terms of speed and efficiency. The method was evaluated by regenerating experimental patterns from predictions and using the <i>R</i>-weighted profile as the agreement factor. This assessment allowed us to confirm the accuracy of the results.</p>","PeriodicalId":14950,"journal":{"name":"Journal of Applied Crystallography","volume":"57 Pt 3","pages":"831-841"},"PeriodicalIF":6.1,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11151672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141283745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>MatchMaps</i>: non-isomorphous difference maps for X-ray crystallography.","authors":"Dennis E Brookner, Doeke R Hekstra","doi":"10.1107/S1600576724003510","DOIUrl":"10.1107/S1600576724003510","url":null,"abstract":"<p><p>Conformational change mediates the biological functions of macromolecules. Crystallographic measurements can map these changes with extraordinary sensitivity as a function of mutations, ligands and time. A popular method for detecting structural differences between crystallographic data sets is the isomorphous difference map. These maps combine the phases of a chosen reference state with the observed changes in structure factor amplitudes to yield a map of changes in electron density. Such maps are much more sensitive to conformational change than structure refinement is, and are unbiased in the sense that observed differences do not depend on refinement of the perturbed state. However, even modest changes in unit-cell properties can render isomorphous difference maps useless. This is unnecessary. Described here is a generalized procedure for calculating observed difference maps that retains the high sensitivity to conformational change and avoids structure refinement of the perturbed state. This procedure is implemented in an open-source Python package, <i>MatchMaps</i>, that can be run in any software environment supporting <i>PHENIX</i> [Liebschner <i>et al.</i> (2019). <i>Acta Cryst.</i> D<b>75</b>, 861-877] and <i>CCP4</i> [Agirre <i>et al.</i> (2023). <i>Acta Cryst.</i> D<b>79</b>, 449-461]. Worked examples show that <i>MatchMaps</i> 'rescues' observed difference electron-density maps for poorly isomorphous crystals, corrects artifacts in nominally isomorphous difference maps, and extends to detecting differences across copies within the asymmetric unit or across altogether different crystal forms.</p>","PeriodicalId":14950,"journal":{"name":"Journal of Applied Crystallography","volume":"57 Pt 3","pages":"885-895"},"PeriodicalIF":6.1,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11151677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141283718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The <i>pypadf</i> package: computing the pair angle distribution function from fluctuation scattering data.","authors":"Andrew V Martin, Patrick Adams, Jack Binns","doi":"10.1107/S1600576724002796","DOIUrl":"10.1107/S1600576724002796","url":null,"abstract":"<p><p>The pair angle distribution function (PADF) is a three- and four-atom correlation function that characterizes the local angular structure of disordered materials, particles or nanocrystalline materials. The PADF can be measured using X-ray or electron fluctuation diffraction data, which can be collected by scanning or flowing a structurally disordered sample through a focused beam. It is a natural generalization of established pair distribution methods, which do not provide angular information. The software package <i>pypadf</i> provides tools to calculate the PADF from fluctuation diffraction data. The package includes tools for calculating the intensity correlation function, which is a necessary step in the PADF calculation and also the basis for other fluctuation scattering analysis techniques.</p>","PeriodicalId":14950,"journal":{"name":"Journal of Applied Crystallography","volume":"57 Pt 3","pages":"877-884"},"PeriodicalIF":6.1,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11151669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141283747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bragg Spot Finder (BSF): a new machine-learning-aided approach to deal with spot finding for rapidly filtering diffraction pattern images.","authors":"Jianxiang Dong, Zhaozheng Yin, Dale Kreitler, Herbert J Bernstein, Jean Jakoncic","doi":"10.1107/S1600576724002450","DOIUrl":"10.1107/S1600576724002450","url":null,"abstract":"<p><p>Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages (<i>Dozor</i> and <i>DIALS</i>), demonstrating that this is an appropriate framework to support future extensions and improvements.</p>","PeriodicalId":14950,"journal":{"name":"Journal of Applied Crystallography","volume":"57 Pt 3","pages":"670-680"},"PeriodicalIF":6.1,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11151665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141283743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ray-tracing analytical absorption correction for X-ray crystallography based on tomographic reconstructions","authors":"Lu, Y., Duman, R., Beilsten-Edmands, J., Winter, G., Basham, M., Evans, G., Kamps, J.J.A.G., Orville, A.M., Kwong, H.-S., Beis, K., Armour, W., Wagner, A.","doi":"10.1107/s1600576724002243","DOIUrl":"https://doi.org/10.1107/s1600576724002243","url":null,"abstract":"","PeriodicalId":14950,"journal":{"name":"Journal of Applied Crystallography","volume":"6 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Upgrade of crystallography beamline BL19U1 at the Shanghai Synchrotron Radiation Facility","authors":"Xiao, Q., Wu, T., Bao, K., Tang, J., Zhang, Y., Zhang, W., Zhu, Z., Gu, Y., Zhou, S., Li, C., Lu, L., Jiang, L., Liu, Y., Qin, W.","doi":"10.1107/s1600576724002188","DOIUrl":"https://doi.org/10.1107/s1600576724002188","url":null,"abstract":"","PeriodicalId":14950,"journal":{"name":"Journal of Applied Crystallography","volume":"14 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}